模型数据融合:发展贝叶斯反演以约束平衡和模态结构
Model Data Fusion: developing Bayesian inversion to constrain equilibrium and mode structure
摘要:
最近,一个新的概率的“数据融合”框架的基础上贝叶斯原则已被开发的JET和W7-AS。贝叶斯分析框架在诊断数据和信号前向模型中的不确定性和相互依赖性中折叠,以及等离子体状态的先验知识,以产生内部磁结构的预测。被称为MINERVA的框架的特征(J. Svensson,A. Werner,等离子体物理和受控聚变50,085022,2008)是在不使用力平衡模型的情况下对磁通量表面的推断。我们讨论了一个新项目的结果,开发贝叶斯反演工具,旨在(1)区分竞争的平衡理论,捕捉不同的物理,使用MAST球形托卡马克;(2)测试的预测MHD理论,特别是模式结构,使用H-1 Heliac。
英文摘要:
Recently, a new probabilistic "data fusion" framework based on Bayesian principles has been developed on JET and W7-AS. The Bayesian analysis framework folds in uncertainties and inter-dependencies in the diagnostic data and signal forward-models, together with prior knowledge of the state of the plasma, to yield predictions of internal magnetic structure. A feature of the framework, known as MINERVA (J. Svensson, A. Werner, Plasma Physics and Controlled Fusion 50, 085022, 2008), is the inference of magnetic flux surfaces without the use of a force balance model. We discuss results from a new project to develop Bayesian inversion tools that aim to (1) distinguish between competing equilibrium theories, which capture different physics, using the MAST spherical tokamak; and (2) test the predictions of MHD theory, particularly mode structure, using the H-1 Heliac.
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